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Information about AI from the News, Publications, and Conferences

If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."

However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …

AI – which Margaris prefers to call "the influence of machine learning or deep learning" – is starting to be felt across the insurtech, fintech and associated industries, he said. "AI, through machine learning and deep learning, will eventually become the entrepreneur of the future--and we humans need to compete against it." A company still needs to have a compelling business case that attracts clients, but AI, machine learning and deep learning for sure will be part of the equation to compete successfully in their space." Margaris has reiterated what has been the most important technology lesson learned over the past four decades, and continues to be the lesson going forward with each new technology wave.

If we sum up all the available numbers for AI research investments (including other government funding like the $93.5M awarded to IVADO by the Canada Research Excellence Fund last September, as well as private funds invested in public or semi-public labs) we end up with close to $500M in funding across the country. Beyond that, when we look up other domains that work hand in hand with AI, such as Big Data, cloud infrastructure and the like, that number grows even higher. What made Silicon Valley's talent pump work up to now was its ecosystem of large firms and venture capital feeding startups, as well as research who in turn generate the innovations to push the large firms forward. With investments from the federal and provincial governments in research, as well as from Big Tech, the Canadian talent pump is growing quickly.

The industrial revolution allowed us to build products at faster rates we had ever seen, and allowed us to scale up our creations to sizes never possible before. Just like machines whose strength is hundreds, if not thousands of times stronger than us, AI's intelligence will be hundreds, if not thousands of times smarter than us. Physical problems like will be solved thousands of times faster than humans could. Machines removed the physical constraints of humans and freed us to pursue more intellectual paths like the information industry, and AI will remove our mental constraints.

Last year was huge for advancements in artificial intelligence and machine learning. The idea has been around for decades, but combining it with large (or deep) neural networks provides the power needed to make it work on really complex problems (like the game of Go). Invented by Ian Goodfellow, now a research scientist at OpenAI, generative adversarial networks, or GANs, are systems consisting of one network that generates new data after learning from a training set, and another that tries to discriminate between real and fake data. The hope is that techniques that have produced spectacular progress in voice and image recognition, among other areas, may also help computers parse and generate language more effectively.

This is the second part in a series where we analyze thousands of articles from tech news sites in order to get insights and trends about startups. So, if a sample mentions an IoT pacemaker startup, it should get the IoT tag in addition to the Health tag. Tagging the data was a similar process to the previous classifier, except that this time we took special care in tagging every sample with all the relevant categories. At this point, we are ready to repeat the same experiment we did in the previous post: classifying 100 articles and seeing what happens.

This is the final part in a series where we use machine learning and natural language processing to analyze articles published in tech news sites in order to gain insights about the state of the startup industry. Let's visualize the coverage per industry for all the articles published in the last ten years to find out: The most popular startup industry in the last ten years has been Mobile. Right when this industry started to gain visibility in 2013, Oculus Rift was the top keyword by a wide margin. The only way to perform an analysis like this is using machine learning and natural language processing, since there's no way we can get a person to read through and interpret 270,000 articles.

Last year was huge for advancements in artificial intelligence and machine learning. The idea has been around for decades, but combining it with large (or deep) neural networks provides the power needed to make it work on really complex problems (like the game of Go). Invented by Ian Goodfellow, now a research scientist at OpenAI, generative adversarial networks, or GANs, are systems consisting of one network that generates new data after learning from a training set, and another that tries to discriminate between real and fake data. The hope is that techniques that have produced spectacular progress in voice and image recognition, among other areas, may also help computers parse and generate language more effectively.

Intel's acquisition earlier this month of Nervana Systems is another example of how startups are preparing to disrupt the worlds largest industries using Artificial Intelligence. DCVC, a venture capital fund that invests in entrepreneurs applying cognitive computing, big data and IT infrastructure technologies to transform giant industries. The company wrote this overview on the services Nervana Systems is already deploying with the goal to disrupt the world's largest industries. Source: Standing on the Shore*: How AI is Disrupting the World's largest Industries by DCVC.

Instead, Descartes relies on 4 petabytes of satellite imaging data and a machine learning algorithm to figure out how healthy the corn crop is from space. Grain elevator operators, ethanol producers, commodities traders, hedge funds, insurance companies, and even the farmers growing the corn will all look to the USDA's August crop report being released August 12th to try and understand how the supply side of the corn market will behave. Descartes says it can consistently out-predict the USDA's corn estimates Descartes, which launched in 2014, began releasing corn yield estimates ahead of the USDA's August crop report last year. Now new nanosatellite constellations, like the one run by satellite imaging startup Planet, are taking snapshots of the entire globe at 3- to 5-meter resolutions every day.